Quantized Context-Based Leaky Integrate and Fire Neurons for Efficient Recurrent Spiking Neural Networks in 45nm CMOS

The proposed quantized context-based leaky integrate and fire (qCLIF) neuron model enables efficient implementation of recurrent spiking neural networks (RSNNs) in digital hardware, achieving high accuracy on gesture recognition tasks with significantly fewer parameters compared to other models.